ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BEDFORD, MA · 2024
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/bedford/2024-annual-report
Yearly Traffic Safety Analysis
229 CRASHES IN
BEDFORD, MA
2024
In Bedford, total traffic crashes remained stable year-over-year, with 229 incidents in 2024 compared to 227 in 2023, an increase of less than 1%. While the overall volume of crashes and injuries was nearly unchanged, the most notable shift was the occurrence of 3 fatalities in 2024, whereas none were recorded in the prior year.
229
▲ 0.9%was 227
Total Crash Events
3
Persons Killed
48
Persons Injured
2
▼ -71.4%was 7
Hit-and-Run Crashes
Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 1 crash with unreported severity is not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in crash volume is stable, with a slight increase of just two incidents from 227 in 2023 to 229 in 2024. The total number of injuries remained unchanged at 48 in both periods. However, a significant development is the increase in traffic fatalities from zero in 2023 to three in 2024.
2
Hit-and-Run Crashes — 2024
▼ -71.4% vs prior (7)
Hit-and-run crashes saw a significant decrease between the two periods. The total count of hit-and-run incidents fell from 7 in 2023 to 2 in 2024. Consequently, the hit-and-run rate dropped from 3.1% of all crashes in the prior year to 0.9% in the current year.
Vulnerable Road User Casualties
2
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
1
Pedestrians Injured
2
Cyclists Injured
45
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes showed high consistency between the two years. Wednesday was the peak day for crashes in both 2024 (55 crashes) and 2023 (50 crashes). The peak hour for incidents shifted slightly, from 3 p.m. in 2023 (29 crashes) to 4 p.m. in 2024 (27 crashes), indicating a consistent concentration of crashes during the afternoon commute.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes worsened in 2024 compared to the prior year, primarily due to an increase in fatal incidents. There were 3 fatal crashes in 2024, resulting in 3 deaths, up from zero in 2023. The proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) saw a slight increase from 16.7% in 2023 to 17.9% in 2024.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors remained the same across both periods, with "Followed too closely" and "Failed to yield right of way" ranking first and second, respectively. The count of crashes attributed to following too closely increased by 16%, from 49 incidents in 2023 to 57 in 2024. A more significant change was observed in crashes involving "Failure to keep in proper lane or running off road," which saw a 75% increase in count from 12 incidents in 2023 to 21 in 2024.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crash conditions were largely consistent year-over-year. The majority of incidents in both 2024 and 2023 occurred during daylight (74.7% and 72.7% of crashes, respectively) and on dry road surfaces (79.0% and 80.2%, respectively). There was no significant shift in the overall proportion of crashes happening in adverse conditions, although incidents involving snow decreased from 14 in 2023 to 5 in 2024.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field
Vehicles & Demographics
Vehicle and person demographics involved in crashes showed little change year-over-year. The top vehicle makes were consistent, led by Toyota and Honda in both periods. Similarly, the age distribution of persons involved remained stable, with the 26-34 age group being the largest cohort in both 2024 (86 persons) and 2023 (89 persons).
Top Vehicle Makes (408 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
11 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (514 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones saw some changes. While incidents were most frequent in 25 and 30 mph zones in 2024, there was a sharp decrease in crashes in 55 mph zones, falling from 44 in 2023 to 17 in 2024. The three fatal crashes in 2024 occurred in 30 mph, 35 mph, and 65 mph zones, with one fatality recorded in each.
Fatal crashes by zone: 30 mph: 1 of 59 (1.695%) · 35 mph: 1 of 29 (3.448%) · 65 mph: 1 of 19 (5.263%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Posted speed limit at crash location
Data Sources & Methodology
Primary Data Source
All crash data in this report is sourced from Massachusetts Crash Data (MassDOT CDV), accessed programmatically via the Arcgis_yearly Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.
Data Retrieval
- Access method: Arcgis_yearly Open Data API (SoQL queries)
- Data format: Structured JSON via REST API
- Record types queried: Crash events, person records, and vehicle unit records
- Date filter applied: 2024-01-01 through 2024-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-01-01 through 2024-12-31 (366 days)
- Geographic scope: BEDFORD, MA
- Total crash records analyzed: 229
- Total persons involved: 532
- Total vehicles involved: 408
Analytical Methodology
- Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
- Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
- Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
- Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
- Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
- Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
- AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.
Limitations & Disclaimers
- Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
- Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
- Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
- AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
- Percentages are calculated from reported data and are subject to rounding.
Non-Affiliation Disclosure
This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.
Data License
The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.
Corrections & Feedback
If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.
Suggested Citation
ThatCarHitMe.com (Injuria.ai). "BEDFORD, MA Crash Intelligence Report: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/bedford/2024-annual-report
About the Publisher
ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.
Questions about this report's data or methodology: data@injuria.ai
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2024-01-01 – 2024-12-31
Generated: June 21, 2026 · All rights reserved